Gene expression and proliferation biomarkers for antidepressant treatment resistance

The Wnt signaling pathway in major depressive disorder: A systematic review of human studies

Despite uncertainty about the specific molecular mechanisms driving major depressive disorder (MDD), the Wnt signaling pathway stands out as a potentially influential factor in the pathogenesis of MDD. Known for its role in intercellular communication, cell proliferation, and fate, Wnt signaling has been implicated in diverse biological phenomena associated with MDD, spanning neurodevelopmental to neurodegenerative processes. In this systematic review, we summarize the functional differences in protein and gene expression of the Wnt signaling pathway, and targeted genetic association studies, to provide an integrated synthesis of available human data examining Wnt signaling in MDD. Thirty-three studies evaluating protein expression (n = 15), gene expression (n = 9), or genetic associations (n = 9) were included. Only fifteen demonstrated a consistently low overall risk of bias in selection, comparability, and exposure. We found conflicting observations of limited and distinct Wnt signaling components across diverse tissue sources. These data do not demonstrate involvement of Wnt signaling dysregulation in MDD. Given the well-established role of Wnt signaling in antidepressant response, we propose that a more targeted and functional assessment of Wnt signaling is needed to understand its role in depression pathophysiology. Future studies should include more components, assess multiple tissues concurrently, and follow a standardized approach.

Identifying Depression Through Machine Learning Analysis of Omics Data: Scoping Review

BACKGROUND

Depression is one of the most common mental disorders that affects >300 million people worldwide. There is a shortage of providers trained in the provision of mental health care, and the nursing workforce is essential in filling this gap. The diagnosis of depression relies heavily on self-reported symptoms and clinical interviews, which are subject to implicit biases. The omics methods, including genomics, transcriptomics, epigenomics, and microbiomics, are novel methods for identifying the biological underpinnings of depression. Machine learning is used to analyze genomic data that includes large, heterogeneous, and multidimensional data sets.

OBJECTIVE

This scoping review aims to review the existing literature on machine learning methods for omics data analysis to identify individuals with depression, with the goal of providing insight into alternative objective and driven insights into the diagnostic process for depression.

METHODS

This scoping review was reported following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Searches were conducted in 3 databases to identify relevant publications. A total of 3 independent researchers performed screening, and discrepancies were resolved by consensus. Critical appraisal was performed using the Joanna Briggs Institute Critical Appraisal Checklist for Analytical Cross-Sectional Studies.

RESULTS

The screening process identified 15 relevant papers. The omics methods included genomics, transcriptomics, epigenomics, multiomics, and microbiomics, and machine learning methods included random forest, support vector machine, k-nearest neighbor, and artificial neural network.

CONCLUSIONS

The findings of this scoping review indicate that the omics methods had similar performance in identifying omics variants associated with depression. All machine learning methods performed well based on their performance metrics. When variants in omics data are associated with an increased risk of depression, the important next step is for clinicians, especially nurses, to assess individuals for symptoms of depression and provide a diagnosis and any necessary treatment.

Contribution of the Opioid System to the Antidepressant Effects of Fluoxetine

BACKGROUND

Selective serotonin reuptake inhibitors such as fluoxetine have a limited treatment efficacy. The mechanism by which some patients respond to fluoxetine while others do not remains poorly understood, limiting treatment effectiveness. We have found the opioid system to be involved in the responsiveness to fluoxetine treatment in a mouse model for anxiety- and depressive-like behavior.

METHODS

We analyzed gene expression changes in the dentate gyrus of mice chronically treated with corticosterone and fluoxetine. After identifying a subset of genes of interest, we studied their expression patterns in relation to treatment responsiveness. We further characterized their expression through in situ hybridization and the analysis of a single-cell RNA sequencing dataset. Finally, we behaviorally tested mu and delta opioid receptor knockout mice in the novelty suppressed feeding test and the forced swim test after chronic corticosterone and fluoxetine treatment.

RESULTS

Chronic fluoxetine treatment upregulates proenkephalin expression in the dentate gyrus, and this upregulation is associated with treatment responsiveness. The expression of several of the most significantly upregulated genes, including proenkephalin, is localized to an anatomically and transcriptionally specialized subgroup of mature granule cells in the dentate gyrus. We have also found that the delta opioid receptor contributes to some, but not all, of the behavioral effects of fluoxetine.

CONCLUSIONS

These data indicate that the opioid system is involved in the antidepressant effects of fluoxetine, and this effect may be mediated through the upregulation of proenkephalin in a subpopulation of mature granule cells.

Identification of a Novel Functional Non-synonymous Single Nucleotide Polymorphism in Frizzled Class Receptor 6 Gene for Involvement in Depressive Symptoms

Although numerous susceptibility loci for depression have been identified in recent years, their biological function and molecular mechanism remain largely unknown. By using an exome-wide association study for depressive symptoms assessed by the Center for Epidemiological Studies Depression (CES-D) score, we discovered a novel missense single nucleotide polymorphism (SNP), rs61753730 (Q152E), located in the fourth exon of the frizzled class receptor 6 gene (FZD6), which is a potential causal variant and is significantly associated with the CES-D score. Computer-based in silico analysis revealed that the protein configuration and stability, as well as the secondary structure of FZD6 differed greatly between the wild-type (WT) and Q152E mutant. We further found that rs61753730 significantly affected the luciferase activity and expression of FZD6 in an allele-specific way. Finally, we generated Fzd6-knockin (Fzd6-KI) mice with rs61753730 mutation using the CRISPR/Cas9 genome editing system and found that these mice presented greater immobility in the forced swimming test, less preference for sucrose in the sucrose preference test, as well as decreased center entries, center time, and distance traveled in the open filed test compared with WT mice after exposed to chronic social defeat stress. These results indicate the involvement of rs61753730 in depression. Taken together, our findings demonstrate that SNP rs61753730 is a novel functional variant and plays an important role in depressive symptoms.

The pharmacogenomics of selective serotonin reuptake inhibitors

Antidepressant medications are frequently used as the first line of treatment for depression. However, their effectiveness is highly variable and influenced by genetic factors. Recently, pharmacogenetic studies, including candidate-gene, genome-wide association studies or polygenic risk scores, have attempted to uncover the genetic architecture of antidepressant response. Genetic variants in at least 27 genes are linked to antidepressant treatment response in both coding and non-coding genomic regions, but evidence is largely inconclusive due to the high polygenicity of the trait and limited cohort sizes in published studies. Future studies should increase the number and diversity of participants to yield sufficient statistical power to characterize the genetic underpinnings and biological mechanisms of treatment response, improve results generalizability and reduce racial health-related inequities.

Transcriptional biomarkers of response to pharmacological treatments in severe mental disorders: A systematic review

Variation in the expression level and activity of genes involved in drug disposition and action in tissues of pharmacological importance have been increasingly investigated in patients treated with psychotropic drugs. Findings are promising, but reliable predictive biomarkers of response have yet to be identified. Here we conducted a PRISMA-compliant systematic search of PubMed, Scopus and PsycInfo up to 12 September 2020 for studies investigating RNA expression levels in cells or biofluids from patients with major depressive disorder, schizophrenia or bipolar disorder characterized for response to psychotropic drugs (antidepressants, antipsychotics or mood stabilizers) or adverse effects. Among 5497 retrieved studies, 123 (63 on antidepressants, 33 on antipsychotics and 27 on mood stabilizers) met inclusion criteria. Studies were either focused on mRNAs (n = 96), microRNAs (n = 19) or long non-coding RNAs (n = 1), with only a minority investigating both mRNAs and microRNAs levels (n = 7). The most replicated results include genes playing a role in inflammation (antidepressants), neurotransmission (antidepressants and antipsychotics) or mitochondrial function (mood stabilizers). Compared to those investigating response to antidepressants, studies focused on antipsychotics or mood stabilizers more often showed lower sample size and lacked replication. Strengths and limitations of available studies are presented and discussed in light of the specific designs, methodology and clinical characterization of included patients for transcriptomic compared to DNA-based studies. Finally, future directions of transcriptomics of psychopharmacological interventions in psychiatric disorders are discussed.

Can (immune and other) gene expression help us to treat depression?

It is becoming clearer that it might be a combination of different biological processes such as genetic, environmental, and psychological factors, together with immune system, stress response, brain neuroplasticity and the regulation of neurotransmitters, that leads to the development of major depressive disorder (MDD). A growing number of studies have tried to investigate the underlying mechanisms of MDD by analysing the expression levels of genes (mRNA) involved in such biological processes. In this review, I have highlighted a possible key role that gene expression might play in the treatment of MDD. This is critical because many patients do not respond to antidepressant treatment or can experience side effects, causing treatment to be interrupted. Unfortunately, selecting the best antidepressant for each individual is still largely a matter of making an informed guess.

High-throughput screening and genome-wide analyses of 44 anticancer drugs in the 1000 Genomes cell lines reveals an association of the NQO1 gene with the response of multiple anticancer drugs

Cancer patients exhibit a broad range of inter-individual variability in response and toxicity to widely used anticancer drugs, and genetic variation is a major contributor to this variability. To identify new genes that influence the response of 44 FDA-approved anticancer drug treatments widely used to treat various types of cancer, we conducted high-throughput screening and genome-wide association mapping using 680 lymphoblastoid cell lines from the 1000 Genomes Project. The drug treatments considered in this study represent nine drug classes widely used in the treatment of cancer in addition to the paclitaxel + epirubicin combination therapy commonly used for breast cancer patients. Our genome-wide association study (GWAS) found several significant and suggestive associations. We prioritized consistent associations for functional follow-up using gene-expression analyses. The NAD(P)H quinone dehydrogenase 1 (NQO1) gene was found to be associated with the dose-response of arsenic trioxide, erlotinib, trametinib, and a combination treatment of paclitaxel + epirubicin. NQO1 has previously been shown as a biomarker of epirubicin response, but our results reveal novel associations with these additional treatments. Baseline gene expression of NQO1 was positively correlated with response for 43 of the 44 treatments surveyed. By interrogating the functional mechanisms of this association, the results demonstrate differences in both baseline and drug-exposed induction.

In the burgeoning field of personalized medicine, genetic variation is recognized as a major contributor to patients’ differential responses to drugs. Lymphoblastoid cell lines (LCLs) are a consistent and convenient representation of cells used for in vitro research. Human genome sequencing with LCLs can identify new genes that influence individuals’ drug responses, including the dose-response relationship, which describes the relationship between physiological response and the amount of exposure to a substance. In this work, we conduct high-throughput screening and genome-wide association mapping using 680 LCLs from the 1000 Genomes Project to identify new genes that influence individual response to 44 widely used anticancer drugs. We found the NQO1 gene to be associated with the dose-response of several drugs, namely arsenic trioxide, erlotinib, trametinib, and the paclitaxel + epirubicin combination, and performed follow-up analyses to better understand its functional role in drug response. Our results indicate NQO1 expression is correlated with increased drug resistance and provide some evidence that SNP rs1800566 influences drug response by altering protein activity for these four treatments. With further research, NQO1 has potential use as a therapeutic target, for example, suppressing NQO1 expression to increase sensitivity to particular drugs.

Gene expression studies in Depression development and treatment: an overview of the underlying molecular mechanisms and biological processes to identify biomarkers

A combination of different risk factors, such as genetic, environmental and psychological factors, together with immune system, stress response, brain neuroplasticity and the regulation of neurotransmitters, is thought to lead to the development of major depressive disorder (MDD). A growing number of studies have tried to investigate the underlying mechanisms of MDD by analysing the expression levels of genes involved in such biological processes. These studies have shown that MDD is not just a brain disorder, but also a body disorder, and this is mainly due to the interplay between the periphery and the Central Nervous System (CNS). To this purpose, most of the studies conducted so far have mainly dedicated to the analysis of the gene expression levels using postmortem brain tissue as well as peripheral blood samples of MDD patients. In this paper, we reviewed the current literature on candidate gene expression alterations and the few existing transcriptomics studies in MDD focusing on inflammation, neuroplasticity, neurotransmitters and stress-related genes. Moreover, we focused our attention on studies, which have investigated mRNA levels as biomarkers to predict therapy outcomes. This is important as many patients do not respond to antidepressant medication or could experience adverse side effects, leading to the interruption of treatment. Unfortunately, the right choice of antidepressant for each individual still remains largely a matter of taking an educated guess.

Potential depression and antidepressant-response biomarkers in human lymphoblast cell lines from treatment-responsive and treatment-resistant subjects: roles of SSRIs and omega-3 polyunsaturated fatty acids

While several therapeutic strategies exist for depression, most antidepressant drugs require several weeks before reaching full biochemical efficacy and remission is not achieved in many patients. Therefore, biomarkers for depression and drug-response would help tailor treatment strategies. This study made use of banked human lymphoblast cell lines (LCLs) from normal and depressed subjects; the latter divided into remitters and non-remitters. Due to the fact that previous studies have shown effects on growth factors, cytokines, and elements of the cAMP-generating system as potential biomarkers for depression and antidepressant action, these were examined in LCLs. Initial gene and protein expression profiles for signaling cascades related to neuroendocrine and inflammatory functions differ among the three groups. Growth factor genes, including VEGFA and BDNF were significantly down-regulated in cells from depressed subjects. In addition, omega-3 polyunsaturated fatty acids (n-3 PUFAs) have been reported to act as both antidepressants and anti-inflammatories, but the mechanisms for these effects are not established. Here we showed that n-3 PUFAs and escitalopram (selective serotonin reuptake inhibitors, SSRIs) treatment increased adenylyl cyclase (AC) and BDNF gene expression in LCLs. These data are consistent with clinical observations showing that n-3 PUFA and SSRI have antidepressant affects, which may be additive. Contrary to observations made in neuronal and glial cells, n-3 PUFA treatment attenuated cAMP accumulation in LCLs. However, while lymphoblasts show paradoxical responses to neurons and glia, patient-derived lymphoblasts appear to carry potential depression biomarkers making them an important tool for studying precision medicine in depressive patients. Furthermore, these data validate usefulness of n-3 PUFAs in treatment for depression.

Cell cycle abnormality is a cellular phenotype in OCD

Abnormal indices of cell cycle regulation have been reported in multiple psychiatric disorders. Though reports specific to Obsessive Compulsive Disorder (OCD) are scant, numerous studies have highlighted partly common underlying biology in psychiatric disorders, cell cycle regulation being one such process. In this study, we therefore aimed to explore cell cycle in OCD. To the best of our knowledge, this is the first study to investigate these effects in OCD. We also evaluated the effect of in vitro fluoxetine, commonly used serotonin reuptake inhibitor (SRI) in OCD patients, on cell cycle regulation. The effects of both disease (OCD) and treatment (SRI) were assessed using lymphoblastoid cell lines (LCLs), derived from OCD patients and healthy controls, as a model system. LCLs were treated with 10μM of fluoxetine for 24 h, and the percentage of cells in each phase of the cell cycle was determined by flow cytometry. We observed a lower proportion of cells in the G2/M phase in OCD cases than controls. The findings suggest that cell cycle dysregulation could be peripheral cellular phenotype for OCD. Among cases, all of whom had been systematically characterized for SRI treatment response, LCLs from non-responders to SRI treatment had a lower proportion of cells in G2/M phase than responders.

Citalopram-induced pathways regulation and tentative treatment-outcome-predicting biomarkers in lymphoblastoid cell lines from depression patients

Antidepressant therapy is still associated with delays in symptomatic improvement and low response rates. Incomplete understanding of molecular mechanisms underlying antidepressant effects hampered the identification of objective biomarkers for antidepressant response. In this work, we studied transcriptome-wide expression followed by pathway analysis in lymphoblastoid cell lines (LCLs) derived from 17 patients documented for response to SSRI antidepressants from the Munich Antidepressant Response Signatures (MARS) study upon short-term incubation (24 and 48 h) with citalopram. Candidate transcripts were further validated with qPCR in MARS LCLs from responders (n = 33) vs. non-responders (n = 36) and afterward in an independent cohort of treatment-resistant patients (n = 20) vs. first-line responders (n = 24) from the STAR*D study. In MARS cohort we observed significant associations of GAD1 (glutamate decarboxylase 1; p = 0.045), TBC1D9 (TBC1 Domain Family Member 9; p = 0.014-0.021) and NFIB (nuclear factor I B; p = 0.015-0.025) expression with response status, remission status and improvement in depression scale, respectively. Pathway analysis of citalopram-altered gene expression indicated response-status-dependent transcriptional reactions. Whereas in clinical responders neural function pathways were primarily up- or downregulated after incubation with citalopram, deregulated pathways in non-responders LCLs mainly involved cell adhesion and immune response. Results from the STAR*D study showed a marginal association of treatment-resistant depression with NFIB (p = 0.068) but not with GAD1 (p = 0.23) and TBC1D9 (p = 0.27). Our results propose the existence of distinct pathway regulation mechanisms in responders vs. non-responders and suggest GAD1, TBC1D9, and NFIB as tentative predictors for clinical response, full remission, and improvement in depression scale, respectively, with only a weak overlap in predictors of different therapy outcome phenotypes.

Human Dermal Fibroblast: A Promising Cellular Model to Study Biological Mechanisms of Major Depression and Antidepressant Drug Response

BACKGROUND

Human dermal fibroblasts (HDF) can be used as a cellular model relatively easily and without genetic engineering. Therefore, HDF represent an interesting tool to study several human diseases including psychiatric disorders. Despite major depressive disorder (MDD) being the second cause of disability in the world, the efficacy of antidepressant drug (AD) treatment is not sufficient and the underlying mechanisms of MDD and the mechanisms of action of AD are poorly understood.

OBJECTIVE

The aim of this review is to highlight the potential of HDF in the study of cellular mechanisms involved in MDD pathophysiology and in the action of AD response.

METHODS

The first part is a systematic review following PRISMA guidelines on the use of HDF in MDD research. The second part reports the mechanisms and molecules both present in HDF and relevant regarding MDD pathophysiology and AD mechanisms of action.

RESULTS

HDFs from MDD patients have been investigated in a relatively small number of works and most of them focused on the adrenergic pathway and metabolism-related gene expression as compared to HDF from healthy controls. The second part listed an important number of papers demonstrating the presence of many molecular processes in HDF, involved in MDD and AD mechanisms of action.

CONCLUSION

The imbalance in the number of papers between the two parts highlights the great and still underused potential of HDF, which stands out as a very promising tool in our understanding of MDD and AD mechanisms of action.

Depression and suicide risk prediction models using blood-derived multi-omics data

More than 300 million people worldwide experience depression; annually, ~800,000 people die by suicide. Unfortunately, conventional interview-based diagnosis is insufficient to accurately predict a psychiatric status. We developed machine learning models to predict depression and suicide risk using blood methylome and transcriptome data from 56 suicide attempters (SAs), 39 patients with major depressive disorder (MDD), and 87 healthy controls. Our random forest classifiers showed accuracies of 92.6% in distinguishing SAs from MDD patients, 87.3% in distinguishing MDD patients from controls, and 86.7% in distinguishing SAs from controls. We also developed regression models for predicting psychiatric scales with R² values of 0.961 and 0.943 for Hamilton Rating Scale for Depression-17 and Scale for Suicide Ideation, respectively. Multi-omics data were used to construct psychiatric status prediction models for improved mental health treatment.

Genetic Risk for Psychiatric Disorders and Telomere Length

Background: Previous studies have revealed associations between psychiatric disorder diagnosis and shorter telomere length. Here, we attempt to discern whether genetic risk for psychiatric disorders, or use of pharmacological treatments (i.e., antidepressants), predict shorter telomere length and risk for aging-related disease in a United Kingdom population sample.

Methods: DNA samples from blood were available from 351 participants who were recruited as part of the South East London Community Health (SELCoH) Study, and for which whole-genome genotype data was available. Leukocyte telomere length was characterized using quantitative polymerase chain reactions. Individualized polygenic risk scores for major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ) were calculated using Psychiatric Genomics Consortium summary statistics. We subsequently performed linear models, to discern the impact polygenic risk for psychiatric disorders (an etiological risk factor) and antidepressant use (common pharmacological treatment) have on telomere length, whilst accounting for other lifestyle/health factors (e.g., BMI, smoking).

Results: There were no significant associations between polygenic risk for any of the psychiatric disorders tested and telomere length (p > 0.05). Antidepressant use was significantly associated with shorter telomere length and this was independent from a depression diagnosis or current depression severity (p ≤ 0.01). Antidepressant use was also associated with a significantly higher risk of aging-related disease, which was independent from depression diagnosis (p ≤ 0.05).

Conclusion: Genetic risk for psychiatric disorders is not associated with shorter telomere length. Further studies are now needed to prospectively characterize if antidepressant use increases risk for aging-related disease and telomere shortening, or whether people who age faster and have aging-related diseases are just more likely to be prescribed antidepressants.

International Union of Basic and Clinical Pharmacology CIV: The Neurobiology of Treatment-resistant Depression: From Antidepressant Classifications to Novel Pharmacological Targets

Major depressive disorder is one of the most prevalent and life-threatening forms of mental illnesses and a major cause of morbidity worldwide. Currently available antidepressants are effective for most patients, although around 30% are considered treatment resistant (TRD), a condition that is associated with a significant impairment of cognitive function and poor quality of life. In this respect, the identification of the molecular mechanisms contributing to TRD represents an essential step for the design of novel and more efficacious drugs able to modify the clinical course of this disorder and increase remission rates in clinical practice. New insights into the neurobiology of TRD have shed light on the role of a number of different mechanisms, including the glutamatergic system, immune/inflammatory systems, neurotrophin function, and epigenetics. Advances in drug discovery processes in TRD have also influenced the classification of antidepressant drugs and novel classifications are available, such as the neuroscience-based nomenclature that can incorporate such advances in drug development for TRD. This review aims to provide an up-to-date description of key mechanisms in TRD and describe current therapeutic strategies for TRD before examining novel approaches that may ultimately address important neurobiological mechanisms not targeted by currently available antidepressants. All in all, we suggest that drug targeting different neurobiological systems should be able to restore normal function but must also promote resilience to reduce the long-term vulnerability to recurrent depressive episodes.

The Genetics of Treatment-Resistant Depression: A Critical Review and Future Perspectives

BACKGROUND

One-third of depressed patients develop treatment-resistant depression with the related sequelae in terms of poor functionality and worse prognosis. Solid evidence suggests that genetic variants are potentially valid predictors of antidepressant efficacy and could be used to provide personalized treatments.

METHODS

The present review summarizes genetic findings of treatment-resistant depression including results from candidate gene studies and genome-wide association studies. The limitations of these approaches are discussed, and suggestions to improve the design of future studies are provided.

RESULTS

Most studies used the candidate gene approach, and few genes showed replicated associations with treatment-resistant depression and/or evidence obtained through complementary approaches (e.g., gene expression studies). These genes included GRIK4, BDNF, SLC6A4, and KCNK2, but confirmatory evidence in large cohorts was often lacking. Genome-wide association studies did not identify any genome-wide significant association at variant level, but pathways including genes modulating actin cytoskeleton, neural plasticity, and neurogenesis may be associated with treatment-resistant depression, in line with results obtained by genome-wide association studies of antidepressant response. The improvement of aggregated tests (e.g., polygenic risk scores), possibly using variant/gene prioritization criteria, the increase in the covering of genetic variants, and the incorporation of clinical-demographic predictors of treatment-resistant depression are proposed as possible strategies to improve future pharmacogenomic studies.

CONCLUSIONS

Genetic biomarkers to identify patients with higher risk of treatment-resistant depression or to guide treatment in these patients are not available yet. Methodological improvements of future studies could lead to the identification of genetic biomarkers with clinical validity.

Pharmacogenomics in the treatment of mood disorders: Strategies and Opportunities for personalized psychiatry

Personalized medicine (personalized psychiatry in a specific setting) is a new model towards individualized care, in which knowledge from genomics and other omic pillars (microbiome, epigenomes, proteome, and metabolome) will be combined with clinical data to guide efforts to new drug development and targeted prescription of the existing treatment options. In this review, we summarize pharmacogenomic studies in mood disorders that may lay the foundation towards personalized psychiatry. In addition, we have discussed the possible strategies to integrate data from omic pillars as a future path to personalized psychiatry. So far, the progress of uncovering single nucleotide polymorphisms (SNPs) underpinning treatment efficacy in mood disorders (e.g., SNPs associated with selective serotonin re-uptake inhibitors or lithium treatment response in patients with bipolar disorder and major depressive disorder) are encouraging, but not adequate. Genetic studies have pointed to a number of SNPs located at candidate genes that possibly influence response to; (a) antidepressants COMT, HTR2A, HTR1A, CNR1, SLC6A4, NPY, MAOA, IL1B, GRIK4, BDNF, GNB3, FKBP5, CYP2D6, CYP2C19, and ABCB1 and (b) mood stabilizers (lithium) 5-HTT, TPH, DRD1, FYN, INPP1, CREB1, BDNF, GSK3β, ARNTL, TIM, DPB, NR3C1, BCR, XBP1, and CACNG2. We suggest three alternative and complementary strategies to implement knowledge gained from pharmacogenomic studies. The first strategy can be to implement diagnostic, therapeutic, or prognostic genetic testing based on candidate genes or gene products. The second alternative is an integrative analysis (systems genomics approach) to combine omics data obtained from the different pillars of omics investigation, including genomics, epigenomes, proteomics, metabolomics and microbiomes. The main goal of system genomics is an identification and understanding of biological pathways, networks, and modules underlying drug-response. The third strategy aims to the development of multivariable diagnostic or prognostic algorithms (tools) combining individual's genomic information (polygenic score) with other predictors (e.g., omics pillars, neuroimaging, and clinical characteristics) to finally predict therapeutic outcomes. An integration of molecular science with that of traditional clinical practice is the way forward to drug discoveries and novel therapeutic approaches and to characterize psychiatric disorders leading to a better predictive, preventive, and personalized medicine (PPPM) in psychiatry. With future advances in the omics technology and methodological developments for data integration, the goal of PPPM in psychiatry is promising.